The subject matter disclosed herein relates to a monitoring system and method, more particularly to a monitoring system and method with hybrid wireless and wired communication. The monitoring system can be used as a medical monitoring system to monitor elderly people or people with neurological diseases in a home environment or in an assisted hymn facility.
The currently widely used low power radio networks (Bluetooth. ZigBee, etc) used for monitoring are using mesh architecture or in special cases tree topology, where each device has only one parent. In these topologies the response time and data throughput are not guaranteed and there is no solution to prioritize the messages. Furthermore, the communication quality indicators are highly depending on the actual network topology and on the placement of the devices in the network hierarchy. This behavior of the current low power radio networks does not allow providing time critical services such as activity monitoring, emergency alarm signaling or voice communication.
There is a continuously growing need for monitoring systems in apartments of elderly people that enables patients to measure vital signs without having to go to the medical doctor. These measurable vital signs comprise blood glucose, blood pressure, ECG, body weight etc. The number of devices enabling medical measurements at home is also continuously growing. It is preferred to measure vital signs without disturbing the patient or even without the need of patient interaction. Such systems can measure patient movements using motion sensors or ECG if the patient sits in an armchair equipped with an ECG measurement unit. The sensor devices shall be designed to be wireless devices. This is not only a need of today's technology but enables the patient to measure vital signs far from a central unit.
Monitoring systems and methods e.g. for the above purposes are disclosed in U.S. Pat. No. 3,882,277, U.S. Pat. No. 5,522,396, U.S. Pat. No. 6,093,146, U.S. Pat. No. 6,336,900 B1, U.S. Pat. No. 6,873,256 B2, US 2009/0081951 A1, WO 03/088830 A1. WO 2010/150031 A1 and WO 2011/012914 A1.
FIG. 1 demonstrates an example ZigBee based wireless medical monitoring system. The ZigBee devices are categorized into three groups: network coordinator 10 (NC) which maintains the radio network, routers R1 . . . R5 which are required for the communication to find the path between the sender and receiver devices, and the end devices or sensors S1 . . . S9 which provide the medical or event data.
The end devices can be either stationary or moving, worn by the patient. The number of sensors necessary to monitor a patient depend on the characteristics of the apartment (e.g. number of rooms or sub-areas) and on other circumstances (e.g. illness or status specific medical signals). The known monitoring system consists of a monitoring center 11 where the network coordinator 10 is installed. The rooms or sub-areas A1, A2, A3 are: patient's apartments. In sub-area A1 there are four sensors and two routers, in sub-area A2 there are three sensors and one router and in sub-area A3 there are two sensors and two routers.
This example highlights the weakness point of the known system: different sensors have different communication path lengths, though the response time can vary in a wide range and highly depends on where the sensor is connected to the network. In FIG. 1 the sensors S8 and S9 can communicate via three routers R4, R2, R1 with the network coordinator 10 which causes at least three times propagation delay in the communication.
A further problem is the arriving order of sequential events received by the monitoring center 11. The signals of sensors closer to the network coordinator 10 will be detected earlier than those of other sensors because of the different communication path lengths.
Another important problem is the different data transmission loads of the routers. In the lower layers of the network tree structure the traffic is significantly lower than in the upper layers. For instance in the example of FIG. 1, routers have to serve different numbers of sensors. From maintainability and scalability standpoint the known network architecture is not practical and economical.
Furthermore, lamer radio networks are more error sensitive. If one node fails in a tree network topology, its sub tree will be inaccessible, in a mesh network the communication has to find a different route, and it could generate a large traffic on the network. The remote maintenance operations like firmware updates and device restarts especially in higher network levels could prevent the normal operation for a long period which is not acceptable in emergency signaling applications or other mission critical applications. The tree and mesh networks are very sensitive to the failure mode when one device is continuously transmitting and blocking the radio communication in a channel.
Thus, there is a particular need for a monitoring system and method eliminating the shortcomings of known techniques. There is also a need for a system and a method which can record the monitored events in a right timely order with less expensive transfer and end devices.